基于改进LightGBM的交通模式识别算法

针对交通模式识别中非步行交通模式识别精度低的问题，提出一种改进后的LightGBM算法结合移动端的交通模式分类方法。该方法首先对数据集进行了滤波处理,选取了三轴加速度计、陀螺仪和磁力计这3种传感器数据的时域和频域特征作为模式识别特征量，然后通过采用Filter相关性度量CFS算法对特征进行打分排序,选择最优特征集，最后识别过程采用分层识别算法和基于居民出行规则与一阶隐马尔科夫链改进的K-lightGBM识别算法对交通模式进行识别，同时采用部分传统算法进行对比实验。实验结果表明，该方法不仅能识别多种交通模式，而且对居民的交通模式识别的平均准确率较高，达到了94%。

Identifying Transportation Mode Based on Improved LightGBM Algorithm

Aiming at the low accuracy of the motorized traffic mode identification in the residents traffic mode identification, this paper proposes an improved LightGBM algorithm combined with a traffic mode classification method of mobile terminal. This method filters the data set, selects the time domain and frequency domain features of three kinds of sensor data: triaxial accelerometer, gyroscope and magnetometer as the pattern recognition features, and uses the Filter correlation measure CFS algorithm to sort the scores according to the features, and selects the optimal feature set. The recognition process adopts the K-lightGBM recognition algorithm based on the residents travel rules and the first-order hidden Markov chain. At the same time, some machine learning algorithms are used for comparison experiments. The experimental results show that this method not only can identify multiple modes of traffic, but also has a high average accuracy of residents traffic pattern recognition, reaching 94%.